Enablement of exposure management to handle priced exposure

ABSTRACT

A message may be used to update computing system of a commodity transaction. The message may include a commodity identifier, a date term, a quantity term, and a price term. The price term may include a formula arrangement or reference to a formula arrangement for calculating a fractional exposure of the entity to the commodity on each date in which an event affecting commodity transaction occurs. Each of these fractional exposures may be calculated for each commodity transaction and stored as separate entries in a data structure. The entries may then be updated, queried, and/or reorganized to generate an exposure position.

BACKGROUND

Many organizations rely on computing systems, such as enterpriseresource planning systems, to manage risk. Different accounting andlegal requirements may require organizations to keep track of andaccount for an organization's financial exposure to various risks,including, for example, foreign exchange rate risks, credit risks,interest risks, and commodity price risks. For example, in the metals,mining, and oil industries different pricing formulas may be used todetermine the price of delivered goods. These pricing formulas may, forexample, average the price of a commodity at a futures exchange over apredetermined time period to calculate a final price of the deliveredcommodity.

While existing exposure management computing systems were able tocalculate an organization's exposure to commodities on a fixed pricebasis, it was difficult and cumbersome to configure these systems toefficiently account for variable or floating price exposures as they mayoccur in sales and purchase orders, and then adjust the exposures ascircumstances change, such as when commodities are ordered versusshipped versus delivered versus paid for. Additionally, existingexposure management systems lacked an intuitive methodology to analyzeexposure information at an atomic level and then restructure the atomiclevel exposures into aggregated exposure positions needed to satisfydifferent hedging, accounting, and/or legal requirements. The lack ofsuch an intuitive methodology has made it difficult for organizations toefficiently restructure price exposures into exposure positions.

There is a need for an intuitive methodology in systems and processesthat may enable organizations to quickly and efficiently calculate aninitial exposure and then later adjust exposure positions ascircumstances change, while also enabling an intuitive and efficientrestructuring of price exposures into exposure positions satisfyingspecific operative needs, such as the need to minimize losses due tohigh market volatility or to satisfy hedging, accounting, or legalrequirements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of how commercial documents including acommodities transaction may be used to update exposure positions in anembodiment of the invention.

FIG. 2 shows how data may be transformed between systems in anembodiment of the invention.

FIG. 3 provides an overview of how an exposure position may be created.

FIG. 4 shows an exemplary process in an embodiment of the invention.

FIG. 5 shows an exemplary architecture in an embodiment of theinvention.

DETAILED DESCRIPTION

In an embodiment, a service oriented architecture (SOA) may be used tosend SOA messages between different systems, such as treasury,accounting, logistics, sales, and others in an enterprise resourceplanning (ERP) system. These messages may include a commodityidentifier, an identifier of an action being taken with respect to thecommodity, a date of the action, and a price term. The price term mayinclude a currency identifier and a formula for calculating a price ofthe commodity, such as average of commodity prices on a specifiedcommodity exchange over a specified number days around a specified date.

These messages may be processed and the pricing formula may be parsed tocreate at least one sub raw exposure line item for each commodity priceexposure within a specified time period. Each sub raw exposure line itemmay represent an “atom” of risk at a finest granularity. For example, ina price exposure example, if a final price is based on an average priceover a five day period, then there may be five risk “atoms” or sub rawexposure line items, each including a daily price on each of the fivedays in the five day period. Other embodiments may specify differentatomic levels, for example, quarters, months, or weeks, instead of days.

If the currency specified in a message differs from a default currency,such as a risk free currency, a corresponding currency exposure lineitem may also be created for corresponding raw exposure line items. Thecurrency exposure amount in the currency exposure line item may becalculated differently in various embodiments. For example, in fixedprice line items, the currency exposure amount may be calculateddirectly from the fixed price specified in the line item. Currencyexposure amounts in floating price line items may be estimated fromcommodity forward price curves or actual spot prices, and may beperiodically recalculated at fixed intervals or in response to certainevents, such as a sudden change in spot prices.

Once the messages have been parsed and the appropriate line items havebeen created, the line items may be regrouped to calculate differentexposure positions. For example, monthly price exposure positions mayalso be calculated for different commodities. Price exposure positionsmay also be calculated based on other criteria, such as the action beingtaken with respect to the commodity. For example, exposure positions maybe grouped by forecasted orders, firm commitment orders, deliveredorders, commodities awaiting delivery, commodity type, and so on. Theexposure positions may then be reported or included as a part of areport.

FIG. 1 shows an example of how contracts, purchase orders, and othertypes of commercial documents or status reports may be used to updateraw exposures in an embodiment. The contents of commercial documents,such as contracts, purchase orders, bills of lading, and so forth, maybe embedded in messages, such as SOA messages, and then transmittedbetween different application systems in an ERP system. For example, theagreed terms of contracts may be included in one or more contractmessages 110.

In a contract for the exchange of goods, the contract message 110 mayinclude an identifier of the goods exchanged, a quantity of goods, aprice of the goods, a delivery date, and other agreed upon terms betweenthe parties. A good may, but need not, be identical to a commodity indifferent embodiments. For example, wheat may be both a good that isexchanged and a commodity listed on a commodity exchange. Otheragricultural goods, such as rye may also be exchanged. While rye is nottypically classified as a commodity, the price of rye may be linked tothe price of wheat. Ores in the mining industry, such as, for example,copper ore, may include several different commodities, such as copper,silver, and gold. Some embodiments may include data fields or dataobjects though which a mapping may be formed between particular goodsand commodities. The mapping may, for example, be used to calculate acommodity price exposure associated with the particular goods specifiedin the contract message.

This data may be extracted from the contract and included incorresponding data fields within the message. In the case of an optionscontract to purchase up to 1,000 tons of copper and 5,000 ounces ofsilver for delivery on or after June 1, at a price equal to the averageprice of the goods over the ten days following the date of delivery, thecontracts message 110 may include data fields for the 1,000 tons ofcopper, the 5,000 ounces of silver, the delivery date of June 1, and theprice calculated as the average price of the 10 days following delivery.

In those instances where a pricing formula is used to calculate theprice, such as with respect to the above contract, the terms of thepricing formula may be stored in a separate data structure instead of inthe message. The price field in the message may then include across-reference to the data structure in order to correlate the properpricing formula to the message.

The contract message 110 may originate from a logistics system, wherethe contract data included in the message may be inputted, received,extracted, or parsed to generate the message. The logistics system maysend the contract message to one or more other application systems inthe ERP system or the logistics system may broadcast the message forinterested parties. The logistics system may also parse or otherwiseprocess the data in the message to create one or more raw exposure items111 and 112 for each commodity specified in the message 110 and may sendthe raw exposure items to the exposure management system. In this case,two raw exposure items 111 and 112 may be created; one for the 1,000tons of copper and one for the 5,000 ounces of silver.

Each raw exposure item 111 and 112 may include one or more data fieldsidentifying the commodity, a quantity term(s), a price term(s), a duedate(s) such as a payment date(s) and/or a delivery date(s), and anaction(s) to be taken. Each of these fields may contain data specificenough to identify a particular exposure associated with each contractterm. For example, if the price is based on an average price of thecommodity over a specific period, then the raw exposure items mayinclude several price term exposure entries reflecting a net exposure oneach day included in the period. Thus, in the case of contact message110, the raw exposure items 111 may include a price exposure term foreach of the ten days (June 2 to June 11) after the June 1 delivery dateon which the price is to be calculated. Since the price in this case isbased on an average price over the ten days, the quantity may be evenlyapportioned between the ten days with 100 of the 1,000 tons ( 1/10^(th))assigned to each of the ten days thereby creating ten separate sub rawexposure entries. The same process may be repeated when creating the rawexposure item 112 with respect to the contracted 5,000 ounces of silver.

Other commercial documents may include different terms. For example, thedelivery dates may be based on a delivery formula instead of or inaddition to the price formula. This may occur, for example, if deliveryis made in period installments instead of on a single occasion. In thissituation, the logistics and/or exposure management system may apply thedelivery formula to calculate an exposure term for each commodity oneach delivery date based on the delivery formula and also the priceformula, if necessary. Each exposure term may then be included in thecorresponding raw exposure item. The systems may be configured tocalculate and include the necessary exposure terms in each raw exposureitem.

Once the raw exposure items and its corresponding sub raw exposureitems, or atoms of risk, are complete, mapping rules may be enforced tomap selected exposure terms in the raw exposure items to a particularraw exposure position. In the example shown in FIG. 1, the mapping rulesmay specify that any contractual commodity exposure occurring in June beincluded in the June Contract Raw Exposure 118. As a result, each of theten price exposure terms in raw exposure items 111 and 112, mainly theprice of the 100 tons of copper and 500 ounces of silver, respectively,on each day between June 2 and June 11, may be included in the JuneContract Raw Exposure 118. The Raw Exposure 118 may thus reflect a totalcontractual exposure of 1,000 tons of copper and 5,000 ounces of silverin June.

Some time later, the organization may place a purchase order to exercisepart of its option to purchase up to 1,000 tons of copper and 5,000ounces of silver. As part of this purchase order, the organization mayelect to purchase 250 tons of copper and 1,000 ounces of silver with anexpected delivery date of June 25, under the original price termincluded in the option contract. Once the purchase order has beengenerated, approved, transmitted, or otherwise processed, the relevantterms included in the purchase order may be inputted, extracted, parsed,or otherwise included in a purchase order message 120 at a logisticssystem. These relevant terms may include an identifier of a priorcommercial document to which the current document relates. In this casepurchase order message 120 may include an identifier of the contractcited in contract message 110.

The logistics system may send the purchase order message 120 to one ormore other application systems in the ERP system or the logistics systemmay broadcast the message. An exposure management system may receive themessage and extract, parse, or otherwise process the data in the messageto create one or more raw exposure items 121 and 122 for each commodityspecified in the message 120. Alternatively, the logistics system mayperform this step before transmitting the information to the exposuremanagement system. In the above mentioned example, two raw exposureitems 121 and 122 may be created; one for the 250 tons of copper orderedand one for the 1,000 ounces of silver ordered. An update message mayalso be generated and/or sent to update the June contract raw exposure118 based on the additional information in the purchase order message120

Each raw exposure item 121 and 122 may include one or more data fieldsidentifying the commodity, a quantity term(s), a price term(s), a duedate(s), and/or an action(s) to be taken. In this case, since the priceterms remain unchanged from the original terms in the contract, thefield in the purchase order cross referencing the original contract maybe used to obtain the pricing terms and/or pricing formulas with theoriginal contract as included, for example, in contract message 110.

Thus, referring to the pricing terms cited in contact message 110, theraw exposure items 121 and 122 may include a price exposure term foreach of the ten days (June 26 to July 5) after the June 25 delivery datefor which the price is to be calculated. Since the price in this case isbased on an average price over the ten days, the quantity goods orderedmay be evenly apportioned between each of the ten days with 25 out ofthe 250 tons ( 1/10^(th)) of ordered copper assigned to each of the tendays. The same process may be repeated when creating the raw exposureitem 122 terms with respect to the ordered 1,000 ounces of silver.

Once the raw exposure items and terms are complete, mapping rules may beenforced to map selected exposure terms in the raw exposure items to aparticular raw exposure. In the example shown in FIG. 1, the mappingrules may specify that any contractual commodity exposure occurring inJune be included in the June Contract Raw Exposure 118 and they may alsospecify that any ordered commodity exposure occurring in June beincluded in the June Order Raw Exposure 128.

Applying these mapping rules to the price exposure terms in raw exposureitems 121 and 122 may result in an updating 125 of the June Contract RawExposure 118 to reflect the purchase of 250 tons of copper and 1,000ounces of silver. Since 250 out of the 1,000 tons of copper and 1,000out of the 5,000 ounces of silver available to purchase have beenordered, the options contract exposure to copper and silver should berespectively reduced by 250 tons and 1,000 ounces. The mapping rules maybe used to automatically update previously calculated exposure positionsas well as to automatically calculate new exposure positions.

Applying these mapping rules may also result in each of the qualifyingten price exposure terms in raw exposure items 121 and 122, mainly theprice of the 250 tons of copper and 1,000 ounces of silver,respectively, on each day in June between June 26 and July 5, to beincluded in the June Order Raw Exposure 128. As a result, the June OrderRaw Exposure 128 may reflect a total order exposure of 125 tons ofcopper (25 tons each day between June 26 and June 30) and 500 ounces ofsilver (100 ounces each day between June 26 and June 30) in June.

On June 28, the organization may receive delivery of 100 tons of copperand 500 ounces of silver. Once the document or action confirmingdelivery has been received, approved, transmitted, or otherwiseprocessed, the relevant terms included in the delivery document may beinputted, extracted, parsed, or otherwise included in a good receivedmessage 130 at a logistics system. These relevant terms may include anidentifier of a prior commercial document to which the current documentrelates. In this case good received message 130 may include anidentifier citing the purchase order referred to in purchase ordermessage 120 and/or an identifier citing the contract referred to incontract message 110. Another update 135 message may also be generatedand/or sent to update the June order raw exposure 128 based on thedelivery.

The logistics system may send the goods received message 130 to one ormore other application systems in the ERP system or the logistics systemmay broadcast the message. An exposure management system may receive themessage and extract, parse, or otherwise process the data in the messageto create one or more raw exposure items 131 and 132 for each commodityspecified in the message 130. Alternatively, the logistics system mayperform this step before transmitting the information to the exposuremanagement system. In the above example, two raw exposure items 131 and132 may be created; one for the 100 tons of copper delivered and one forthe 500 ounces of silver delivered.

Each raw exposure item 131 and 132 may include one or more data fieldsidentifying the commodity, a quantity term(s), a price term(s), a duedate(s), and/or an action(s) to be taken. In this case, since the priceterms remain unchanged from the original terms in the contract, theprice field(s) in the original contract may be used to obtain thepricing terms and/or pricing formulas.

Thus, referring to the pricing terms cited in contact message 110, theraw exposure items 131 and 132 may include a price exposure term foreach of the ten days (June 29 to July 8) after the June 28 delivery datefor which the price is to be calculated. Since the price in this case isbased on an average price over the ten days, the quantity of goodsdelivered may be evenly apportioned between each of the ten days with 10out of the 100 tons ( 1/10^(th)) of delivered copper assigned to each ofthe ten days. The same process may be repeated when creating the rawexposure item 132 terms with respect to the delivered 500 ounces ofsilver.

Once the raw exposure items and terms are complete, mapping rules may beenforced to map selected exposure terms in the raw exposure items to aparticular raw exposure and/or exposure position. In the example shownin FIG. 1, the mapping rules may specify that any ordered commodityexposure occurring in June be included in the June Order Raw Exposure128 and they may also specify that any commodity delivery occurring inJune be included in the June Delivery Raw Exposure 138.

Applying these mapping rules to the price exposure terms in raw exposureitems 131 and 132 may result in an updating 135 of the June Contract RawExposure 128 to reflect the delivery of 100 tons of copper and 500ounces of silver. Since 100 out of the 250 tons of copper ordered hasbeen delivered, and 500 out of the 1,000 ounces of silver ordered hasbeen delivered, the order exposure to copper and silver should berespectively reduced by 100 tons and 500 ounces. The mapping rules maybe used to automatically update previously calculated exposures as wellas to automatically calculate new exposures.

Applying these mapping rules may also result in each of the qualifyingten price exposure terms in raw exposure items 131 and 132, mainly theJune price exposures of the 100 tons of copper and 500 ounces of silver,respectively, on June 29 and June 30, being included in the June OrderRaw Exposure 128. As a result, the June Delivery Raw Exposure 138 mayreflect a total order exposure of 20 tons of copper (10 tons each daybetween June 29 and June 30) and 100 ounces of silver (50 ounces eachday between June 29 and June 30).

In sum, FIG. 1 shows how creating separate exposure terms, such as dailyprice exposure terms, for each commodity in raw exposure item recordsand then using mapping rules to map different exposure terms todifferent exposure positions enables the seamless creation and updatingof customized raw exposures.

FIG. 2 shows how data may be transformed between systems to enable thecreation of customizable exposure positions and exposure positionreports. Events representing different phases of commercialtransactions, such as executing, sending, or receiving contracts,options, purchase orders, bills of lading, or other documents may bemonitored by logistics system 201. During this monitoring, the logisticssystem 201 may send and receive messages, such as SOA messages, to otherapplication systems. These messages may include event updates regardingthe different phases of commercial transactions.

Each of these messages may include one or more message data fields 210.The data included in these fields may be obtained from a commercialdocument, another application and/or computing system, an incomingmessage, or another data source. The data may be extracted, parsed,copied, or otherwise transferred to the message data fields 210 in thelogistics system. In some instances, the logistics system 210 may searchmessages, data sources, or documents to identify particular fields, suchas fields 211 to 215 from which data is to be obtained. In otherinstances, the logistics system 210 may scan messages, data sources, ordocuments to identify additional fields that may be included as part ofthe message data fields 210.

Some exemplary message data fields 210 include fields 211 to 215. Amessage type field 211 may indicate the type of message or type ofcommercial document the message data represents. A commodity identifierfield 211 may a identify of a commodity to which the rest of the datapertains. An exposure identifier field 212 may identify an exposurefaced by the organization. Examples of exposures include but are notlimited to a commodity price risk and a currency fluctuation risk. Aformula field 213 may specify a formula used to calculate a commodityprice. The formula field 213 may include a reference to a pricingformula data structure 220 which may include additional data as part ofthe pricing formula from which a commodity price may be calculated.

The price field 214 may include a fixed commodity price. The fixed pricemay be specified at the outset of a commodity transaction, in that theparties may initially agree to a particular fixed price when acommodities contract is commenced. The price field 214 may also becompleted at a future date. For example, if an agreed commodity price iscontingent on an exchange price at a future date, the price field 214may be left blank until the exchange price on the future date isdetermined. At that time, the exchange price may be entered in the pricefield 214. In some instances, a blank price field 214 may indicate thatthe price is variable whereas a non-blank price field 214 may indicatedthat the commodity price is fixed.

Quantity field 215 may include a commodity quantity. In some instances,the quantity field 214 may also include units of measurement as shown.In other instances the unit of measurement may be implied based on oneor more other fields or the units of measurement may be included as partof a separate field.

Message data fields 210 may also include a currency field (not shown).The currency field may specified the currency in which a commoditytransaction is to occur. In some instances, the currency may included aspart of the price field 214, the formula field 213, or the pricingformula data structure 220, though in other instances the currency maybe included in other fields or as part of its own separate field. If thecurrency is different from a default currency, a currency exposureand/or currency exposure position may also be calculated.

The pricing formula data structure 220 may include one or more datafields 221 to 227 used to calculate a commodity price. For example, aline field 221 may identify particular rows of the pricing formula. TheQuote Date 222 field may specify a date a commodity price is to takeeffect. The QN or Quote Name field 223 may be used to identify thecommodity to a price quote source using an identifier recognized by thequote source. The QS or quote source field 224 may specify a source fromwhich the price quote is to be obtained. The % or percent field 225 mayspecify a percent of a commodity order on which the price quoted on thequote date 222 from the quote source 224 is to be based. The quantityfield 226 may specify a quantity of a commodity order on which the pricequoted on the quote date 222 from the quote source 224 is to be based.

The price field 227 may be used to store the quote price from the quotesource 224 for the commodity 223 on the quote date 222. In someinstances, a price from the quote source 224 may be automaticallyentered in the price field 227 on or after the quote date 222. Forexample, the logistics system 201 or another system may retrieve theprice quote from quote source 224 using the quote name 223 recognized bythe quote source 224 on the quote date 222. Alternatively, the quotesource may also send a message to the organization's systems, including,for example, the logistics system 201, on or after the quote date 222that included the quote price 227 and the quote name 223. The logisticssystem may analyze the message and store the price included in themessage in the price field 227.

The logistics system 201 may generate and/or send messages includingdata from the message data structure 210 and pricing formula datastructure 220 to the exposure management system 202. The exposuremanagement system 202 may process the messages, analyze the data, andrestructure the data into a raw exposure data structure 230 and/or asub-raw exposure data structure 240, from which exposure positions 261and 262 may be generated. The sub-raw exposure data structure 240 may beused to store atomic level exposure information. Atomic level exposureinformation may include, for example, exposure information at a finestgranularity. For example, in a price exposure example, if a final priceis based on an average price over a five day period, then the atomiclevel exposure information may include the price on each of the days inthe five day period, since this is the most detailed information and thefinest level of granularity used to calculated the final price.

For example, the raw exposure data structure 230 may include a commodityidentifier field 232. This may, but need not, be copied from thecommodity ID field 211 and/or quote name field 223. The fixed/floatingF/V field 223 may indicate whether a commodity price is fixed orfloating. The F/V field 223 may indicate the price is fixed if each ofthe applicable price fields 213 and/or 227 in the corresponding datastructures 210 and/or 220 include a price term. Otherwise, the F/V field223 may indicate the price is variable or to be determined in thefuture. The due date field 234 may indicate a last delivery date, anorder date, or some other date affective a commodity exposure.

The price field 235 may indicate a calculated price of the commodity.The price field 235 may include data from one or more of the pricefields 214 and/or 227 included in the transmitted message. In the casewhere a pricing formula is specified, the price field 235 may include aresulting price calculated by applying the pricing formula. The quantityfield 236 may include a quantity of commodity involved in a commoditytransaction creating an exposure for the organization.

The data in the raw exposure data structure 230 may also be combinedwith data from the pricing formula data structure 220 included inmessages sent to the exposure management system 202 to create a sub-rawexposure data structure 240. The sub-raw exposure data structure 240 mayresult in an itemization of commodity transactions by date or otherevent. The itemization may enable the data within the sub-raw exposuredata structure 240 to be reorganized into user specified exposurepositions.

In the example shown in FIG. 2, the contract for 5,000 tons of copper isbased on an average price of copper on the London Metals Exchange (LME)on January 1 and February 1, whereas the contract for 10,000 ounces ofsilver is fixed at eighteen U.S. dollars per ounce. The data concerningthe copper and silver transactions included in the raw exposure datastructure 230 and the pricing formula data structure 220 may be combinedso that each individual transaction resulting in an exposure isseparately listed in the sub-raw exposure data structure 240. In thiscase, the transaction involving the 10,000 ounces of silver on January 1for US$18/oz is shown on the first row of the sub-raw exposure datastructure 240. Since the 5,000 ton copper transaction results in twoseparate price exposures, one on January 1 and the other on February 1,two separate entries, one for the exposure on January 1 and the otherfor the exposure on February 1 may be created and included in thesub-raw data structure 240.

The sub-raw data structure 240 may include different data fields. Forexample, a line field 241 may store a reference identifier to correspondthe row in the sub-raw structure 240 to its parent in the raw exposurestructure 230 or in the message structure 210. The line field 241 inthis case includes a “1” for entries relating to the silver transactionand a “2” for entries relating to the copper transaction. The commodityidentifier field 242 may identify the commodity involved in thetransaction. The commodity identifier field 242 may be based on one ormore or a combination of one or more of the commodity identifier fields211, 232, 223, and/or 224. The fixed/floating F/V field 243 may indicatewhether a price in the price field 246 is fixed or variable. The duedate field 244 may indicate a date a transaction is planned to occur.The price field 245 may indicate the price of the commodity on the datein the date field 244. The quantity field 246 may indicate the quantityof the commodity identified in field 242 and involved in the transactionon the date in the date field 244.

A user specified or constructed query may then be executed on the datain the sub raw exposure data structure 240 to generate an exposureposition. In the example shown in FIG. 2, queries grouping the due datefield 245 by month may be run to generate monthly commodity exposurepositions reports for silver and, separately, quarterly reports forcopper. The January exposure position report for silver 261 may includedata from the first row of the sub raw exposure data structure 240pertaining to the 10,000 ounce silver transaction on January 1. TheFirst Quarter exposure position report for copper 262 may includeaggregated data from the second and third rows of the sub raw exposuredata structure 240 pertaining to the 2,500 ton copper pricingtransaction occurring on January 1 and the second 2,500 ton copperpricing transaction occurring on February 1.

FIG. 3 provides a broad overview of how an exposure position 330 may becreated from documents, records, and data sources 311 containingexposure information. Raw exposure information 310 may be included inone or more exposure documents 311. These exposure documents 311 mayinclude contracts, purchase orders, receipts, bills of landing, andother records including data reflecting a commercial transactioninvolving one or more commodities.

Each exposure document 311 may include information about one or morecommodity transactions. A raw exposure item record 312 may be createdfor each commodity transaction included in the exposure document 311.The raw exposure item records 312 may include data and data fieldscontaining information relevant to the commodity transactions, such asthe data fields shown in the raw exposure data structure 230.

One or more sub-raw exposure item records 313 may be created for eachraw exposure item record 312. A separate sub-raw exposure item record313 may be created for each distinct transactional event associated witha commodity transaction in a raw exposure item record 312. For example,if the price of a commodity of a transaction included in a raw exposureitem record 312 is based on an average price of the commodity over athirty day period, then a separate sub-raw exposure item record 313corresponding to each atomic risk may be generated for each day in thethirty day period to account for the price exposure on each of thirtydays. Similarly, if a price of the commodity is dependent on a deliverydate and the delivery is subdivided and split between several dates, aseparate sub-raw exposure item record 313 may be generated for each ofthe dates on which a partial delivery is scheduled to occur to accountfor the price exposure on each of the delivery dates.

Mapping rules 320 may be used to aggregate different sub-raw exposurerecords 313 generated from one or more different raw exposure items 312and exposure documents 311 to a single exposure position 330. Themapping rules 320 may specify the criteria for an exposure position 330.For example, if an exposure position 330 setting forth the monthlyexposure to certain commodities over a one year period is desired, themapping rules 320 may specify the criteria of certain commodities,and/or may specify the year or monthly periods.

Each exposure position 330 may include a header 331, which may includeinformation identifying the exposure position 330 and/or informationidentifying the fields included in the exposure flows 332. Each exposureposition 330 may also include one or more exposure flows 332. Eachexposure flow 332 may include data and changes to data from acorresponding sub-raw exposure item 313 mapped to the exposure position330 by the mapping rules 320.

The data in header 331 and flows 322 of an exposure position 330 maythen be sent to an exposure reporting system 340, where data may bereformatted, consolidated, or otherwise included in a report, analysis,or other finding.

FIG. 4 shows an exemplary process in an embodiment of the invention. Inbox 401, the terms of a commodity transaction, such as a date of thetransaction, a price of the transaction, and a quantity of the commodityinvolved in the transaction, included in a message processed at anexposure management system may be identified. The identified terms mayinclude a data relating to a formula arrangement for calculating afractional exposure of the entity to the commodity on a plurality ofdates on which events related to the commodity transaction may occur.

In box 402, the formula arrangement and the identified terms may be usedto calculate the fractional exposure of the entity to the commodity oneach of the dates on which events related to the commodity transactionmay occur.

In box 403, the calculated fractional exposure for each of the dates maybe stored in a data structure along with other identified terms of thecontract, which may also include a fractional amount of at least oneterm. The fractional amount may correspond to the calculated fractionalexposure, so that if, for example, the final price of the commodity isbased on an average price over two dates, the entries in the datastructure corresponding to the fractional exposure one the first of thetwo dates may also include one-half of the total quantity in thequantity term. Thus, a fractional one-half of the total quantity may befractional amount of the quantity term that is included in the datastructure.

FIG. 5 shows an exemplary architecture in an embodiment of theinvention. Exposure management system 510 may analyze data relating to acommodity transaction 511 received though an electronic message overnetwork 550 and may analyze and perform fractional exposure calculationson the commodity transaction data 511 according to formula arrangement512. Exposure management system 510 may be connect to a network 550.

Network 550 may include a LAN, WAN, bus, or the Internet. Exposuremanagement system 510 may interface with other systems and componentdepending on the application. For example, a network/data storage device560 may be used to store the data structures previously mentioned. Thestorage device 560 may be a part of the ERP system 510. In someembodiments the network storage device 560 may also be separate from theERP system 510 but connected to it through network 550. The storagedevice 560 may contain a hard disk drive, flash memory, or othercomputer readable media capable of storing data.

Logistics systems 570 and other external data sources may also beconnected to network 550. Logistics system 570 may be used to identify acommodity transaction, identify a formula arrangement associated withthe commodity transaction, generate messages including commoditytransaction and/or formula arrangement data, and then send the generatedmessages to the exposure management system. Logistics system 570 mayalso generate and send updated messages reflecting changes to commoditytransactions identified by the logistics system 570.

Each of the systems and devices in FIG. 5 may contain a processingdevice 502, memory 503 storing loaded data or a loaded data structure505, and an communications device 504, all of which may beinterconnected via a system bus. In various embodiments, each of thesystems 510, 560, and 570 may have an architecture with modular hardwareand/or software systems that include additional and/or different systemscommunicating through one or more networks. The modular design mayenable a business to add, exchange, and upgrade systems, including usingsystems from different vendors in some embodiments. Because of thehighly customized nature of these systems, different embodiments mayhave different types, quantities, and configurations of systemsdepending on the environment and organizational demands.

Communications device 504 may enable connectivity between the processingdevices 502 in each of the systems and the network 550 by encoding datato be sent from the processing device 502 to another system over thenetwork 550 and decoding data received from another system over thenetwork 550 for the processing device 502.

In an embodiment, memory 503 may contain different components forretrieving, presenting, changing, and saving data. Memory 503 mayinclude a variety of memory devices, for example, Dynamic Random AccessMemory (DRAM), Static RAM (SRAM), flash memory, cache memory, and othermemory devices. Additionally, for example, memory 503 and processingdevice(s) 502 may be distributed across several different computers thatcollectively comprise a system.

Processing device 502 may perform computation and control functions of asystem and comprises a suitable central processing unit (CPU).Processing device 502 may include a single integrated circuit, such as amicroprocessing device, or may include any suitable number of integratedcircuit devices and/or circuit boards working in cooperation toaccomplish the functions of a processing device. Processing device 502may execute computer programs, such as object-oriented computerprograms, within memory 503.

The foregoing description has been presented for purposes ofillustration and description. It is not exhaustive and does not limitembodiments of the invention to the precise forms disclosed.Modifications and variations are possible in light of the aboveteachings or may be acquired from the practicing embodiments consistentwith the invention. For example, some of the described embodiments mayinclude software or hardware, but some systems and methods consistentwith the present invention may be implemented in both software andhardware.

We claim:
 1. A method for calculating an exposure of an entitycomprising: identifying, using a hardware processing device, datarelating to a transaction of a good, including a commodity associatedwith the good, a payment date term, a delivery date term, a price term,and a quantity term in an electronic message received at a computingsystem of the entity; identifying a plurality of dates from a formulaspecifying a fractional amount of the commodity to be at least one ofpaid for, delivered, or priced on each of the identified dates;calculating, using the hardware processing device, a fractional amountof a respective payment, delivery, or price exposure to the commodity oneach of the plurality of dates from the formula and at least one of therespective identified payment date, delivery date, or price date terms;storing each of the calculated fractional commodity exposures asseparate entries in a data structure; and including at least two of thestored separate entities in different exposure positions based on thedates associated with each of the respective calculated fractionalcommodity exposures.
 2. The method of claim 1, further comprisingresponsive to a subsequent processing of updated data related to thetransaction of the good, calculating an updated fractional exposure tothe commodity and then updating at least one of the stored entries inthe data structure with the updated fractional exposure.
 3. The methodof claim 1, wherein the commodity associated with the good is identifiedthrough a mapping associating different goods with at least onecommodity.
 4. The method of claim 1, wherein the good is the commodityitself.
 5. The method of claim 1, wherein the fractional amount iseither a price representing a percentage of a total price included inthe price term or a percentage of a price in the price term.
 6. Themethod of claim 1, wherein the fractional amount is a quantityrepresenting a percentage of a total quantity included in the quantityterm.
 7. The method of claim 1, wherein the message is a serviceoriented architecture message and the computing system is an exposuremanagement system that calculates the fractional exposure of the entityto various commodities.
 8. The method of claim 6, further comprisingrepeating the method to identify commodities, date terms, price terms,and quantity terms in a plurality of electronic messages, calculateexposures to the commodities, and then store each of the calculatedfractional commodity exposures as separate entries in the datastructure.
 9. The method of claim 1, wherein the price term includes aformula field, the formula field including data identifying the formulastored in a separate data structure.
 10. The method of claim 1, whereinthe formula includes data fields storing a plurality of dates, commodityidentifiers, and commodity quantities and calculating the exposureincludes, for each data record in the formula, identifying a commodityprice on a stored date using a stored commodity identifier and thenmultiplying the identified commodity price and a stored commodityquantity.
 11. The method of claim 9, wherein the commodity price isobtained from an external data source and a commodity identifier, whichidentifies a commodity in a format compatible with the external datasource, is transmitted to the external data source to obtain thecommodity price.
 12. The method of claim 10, wherein the formulaincludes an external data source field and data in the external datasource field is used to identify the external data source to which thecommodity identifier is transmitted.
 13. The method of claim 1, whereinthe calculating the fractional exposure includes calculating currencyexposures when a different currency is included in the price term, andthe calculated currency exposures are stored as a separate entries inthe data structure.
 14. The method of claim 9, wherein storing of eachcalculated commodity exposure includes storing the multiplied identifiedcommodity price and stored commodity quantity, and a plurality of thecommodity quantities stored in the formula are less than a correspondingtotal quantity included in the quantity term.
 15. The method of claim13, wherein storing of each calculated commodity exposure includesstoring a respective commodity quantity stored in the formula and therespective commodity quantity is a fractional amount of its respectivequantity term.
 16. The method of claim 9, wherein the formula providesfor a computation involving data in the price and quantity terms. 17.The method of claim 15, wherein the formula includes a data fieldstoring a percentage of a quantity included in the quantity term. 18.The method of claim 16, wherein the computation includes applying thepercentage stored in the formula to the quantity included in thequantity term to calculate a data record commodity quantity.
 19. Asystem comprising: a processing device including at least one integratedcircuit; a memory device storing a data structure and a formulaspecifying a fractional amount of the commodity to be at least one ofpaid for, delivered, or priced on each of predetermined dates; and acalculating arrangement to perform a mathematical calculation; wherein:the processing device identifies a commodity, a payment date term, adelivery date term, a price term, and a quantity term relating to atransaction of a good in an electronic message; the calculatingarrangement calculates a fractional amount of a respective payment,delivery, or price exposure to the commodity on each of the plurality ofdates in the formula using the formula and at least one of therespective identified payment date, delivery date, or price date terms;and the processing device stores each of the calculated fractionalcommodity exposures as separate entries in the data structure with eachof the separate entries and includes at least two of the stored separateentities in different exposure positions based on the dates associatedwith each of the respective calculated fractional commodity exposures.20. A device including a non-transitory computer readable storage mediumstoring instructions that, when executed by a processing device, causethe processing device to: identify data relating to a transaction of agood, including a commodity associated with the good, a payment dateterm, a delivery date term, a price term, and a quantity term in anelectronic message received at a computing system of the entity;identify a plurality of dates from a formula specifying a fractionalamount of the commodity to be at least one of paid for, delivered, orpriced on each of the identified dates; calculate a fractional amount ofa respective payment, delivery, or price exposure to the commodity oneach of the plurality of dates from the formula and at least one of therespective identified payment date, delivery date, or price date terms;store each of the calculated fractional commodity exposures as separateentries in a data structure; and include at least two of the storedseparate entities in different exposure positions based on the datesassociated with each of the respective calculated fractional commodityexposures.